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Description
The cerebellum is recognized for its role in motor movement, balance, and more recently, social behavior. Cerebellar injury at birth and during critical periods reduces social preference in animal models and increases the risk of autism in humans. Social behavior is commonly assessed with the three-chamber test, where a mouse travels between chambers that contain a conspecific and an object confined under a wire cup. However, this test is unable to quantify interactive behaviors between pairs of mice, which could not be tracked until the recent development of machine learning programs that track animal behavior. In this study, both the three-chamber test and a novel freely-moving social interaction test assessed social behavior in untreated male and female mice, as well as in male mice injected with hM3Dq (excitatory) DREADDs. In the three-chamber test, significant differences were found in the time spent (female: p < 0.05, male: p < 0.001) and distance traveled (female: p < 0.05, male: p < 0.001) in the chamber with the familiar conspecific, compared to the chamber with the object, for untreated male, untreated female, and mice with activated hM3Dq DREADDs. A social memory test was added, where the object was replaced with a novel mouse. Untreated male mice spent significantly more time (p < 0.05) and traveled a greater distance (p < 0.05) in the chamber with the novel mouse, while male mice with activated hM3Dq DREADDs spent more time (p<0.05) in the chamber with the familiar conspecific. Data from the freely-moving social interaction test was used to calculate freely-moving interactive behaviors between pairs of mice and interactions with an object. No sex differences were found, but mice with excited hM3Dq DREADDs engaged in significantly more anogenital sniffing (p < 0.05) and side-side contact (p < 0.05) behaviors. All these results indicate how machine learning allows for nuanced insights into how both sex and chemogenetic excitation impact social behavior in freely-moving mice.
ContributorsNelson, Megan (Author) / Verpeut, Jessica (Thesis director) / Bimonte-Nelson, Heather (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / School of Life Sciences (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2024-05
Description
This review explores popular gambling strategies often believed to guarantee wins, such as card counting and taking advantage of arbitrage. We present a mathematical overview of these systems to evaluate their theoretical effectiveness in ideal conditions by presenting prior research and mathematical proofs. This paper then generates results from these models using Monte Carlo simulations and compares them to data from real-world scenarios. Additionally, we examine reasons that might explain the discrepancies between theoretical and real-world results, such as the potential for dealers to detect and counteract card counting. Ultimately, although these strategies may fare well in theoretical scenarios, they struggle to create long-term winning solutions in casino or online gambling settings.
ContributorsBoyilla, Harsha (Author) / Clough, Michael (Thesis director) / Eikenberry, Steffen (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2024-05
Description
Since the 20th century, Arizona has undergone shifts in agricultural practices, driven by urban expansion and crop irrigation regulations. These changes present environmental challenges, altering atmospheric processes and influencing climate dynamics. Given the potential threats of climate change and drought on water availability for agriculture, further modifications in the agricultural landscape are expected. To understand these land use changes and their impact on carbon dynamics, our study quantified aboveground carbon storage in both cultivated and
abandoned agricultural fields. To accomplish this, we employed Python and various geospatial libraries in Jupyter Notebook files, for thorough dataset assembly and visual, quantitative analysis. We focused on nine counties known for high cultivation levels, primarily located in the lower latitudes of Arizona. Our analysis investigated carbon dynamics across not only abandoned and actively cultivated croplands but also neighboring uncultivated land, for which we estimated the extent. Additionally, we compared these trends with those observed in developed land areas.
The findings revealed a hierarchy in aboveground carbon storage, with currently cultivated lands having the lowest levels, followed by abandoned croplands and uncultivated wilderness. However, wilderness areas exhibited significant variation in carbon storage by county compared to cultivated and abandoned lands. Developed lands ranked highest in aboveground carbon storage, with the median value being the highest. Despite county-wide variations, abandoned croplands generally contained more carbon than currently cultivated areas, with adjacent wilderness lands containing even more than both. This trend suggests that cultivating croplands in the region reduces aboveground carbon stores, while abandonment allows for some replenishment, though only to a limited extent. Enhancing carbon stores in Arizona can be achieved through active restoration efforts on abandoned cropland. By promoting native plant regeneration and boosting aboveground carbon levels, these measures are crucial for improving carbon sequestration. We strongly advocate for implementing this step to facilitate the regrowth of native plants and enhance overall carbon storage in the region.
ContributorsGoodwin, Emily (Author) / Eikenberry, Steffen (Thesis director) / Kuang, Yang (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2024-05
Description
This project involved research into solar thermal and geothermal energy generation as possible solutions to the growing U.S. energy crisis. Background research into this topic revealed the effects of climate and environmental impacts as major variables in determining optimal states. Delving into thermodynamic engineering analyses, the main deliverables of this research were mathematical models to analyze plant efficiency improvements in order to optimize the cost of operating solar thermal and geothermal power plants. The project concludes with possible future research areas relating to this field.
ContributorsRattner, Andrew (Author) / Beyer, Luke (Co-author) / Kwon, Beomjin (Thesis director) / Wilbur, Joshua (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of International Letters and Cultures (Contributor)
Created2024-05
Description
With uses in fields such as medicine, agriculture, and biotechnology, halogenases are useful enzymes in nature which add or substitute halogens onto other molecules. By doing so, they become necessary for biosynthesis and cross-coupling reactions. Halogenases can be classified by three main types of mechanisms: nucleophilic, radical, and electrophilic. From there, they can be further broken down by the halogen involved, the substrate needed, other proteins used, or molecules generated. A notable example is PrnA which is a tryptophan-7 halogenase that falls under the flavin-dependent definition with an electrophilic mechanism. Historically, research on these enzymes was slow until the use of bioinformatics rapidly accelerated discoveries to the point where halogenases like VirX1 can be identified from viruses. By reviewing the literature available on halogenase since their first analysis, a better understanding of their functions can be obtained. Also, with the application of bioinformatics, a phylogenetic analysis on the halogenases present in cyanobacteria can be conducted and compared.
ContributorsUsmani, Hibah (Author) / Zhu, Qiyun (Thesis director) / Neilan, Brett (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Life Sciences (Contributor)
Created2024-05
Description
The characterization of spall microstructural damage metallic samples is critical to predicting and modeling modes of failure under blast, ballistic, and other dynamic loads. In this regard, a key step to improve models of dynamic damage is making appropriate connections between experimental characterization of actual damage in the form of discrete voids distributed over a given volume of the specimens, and the output of the models, which provide a continuous measure of damage, for example, void fraction as a function of position. Hence, appropriate homogenization schemes to estimate, e.g., continuous void fraction estimations from discrete void distributions, are key to calibration and validation of damage models. This project seeks to analyze 3D tomography data to relate the homogenization parameters for the discrete void distributions, i.e., homogenization volume size and step, as well as representative volume element size, to the local length scales, e.g., grain size as well as void size and spacing. Copper disks 10 mm in diameter and 1 mm thick with polycrystalline structures were subjected to flyer plate impacts resulting in shock stresses ranging from 2 to 5 GPa. The spall damage induced in samples by release waves was characterized using X-ray tomography techniques. The resulting data is thresholded to differentiate voids from the matrix and void fraction is obtained via homogenization using various parameterization schemes to characterize void fraction distributions along the shock and transverse directions. The representative volume element is determined by relating void fraction for varying parameterized window sizes to the void fraction in the overall volume. Results of this study demonstrate that the optimal representative volume element (RVE) to represent void fraction within 10% error of the overall sample void fraction for this Hitachi copper sample is .2304 mm3. The RVE is found to contain approximately 255 grains. Statistical volume elements of 1300 µm3 or smaller are used to quantify void fraction as a function of position and while the results along the shock direction, i.e., the presence of a clear peak at the expected location of the spall plane, are expected, the void fraction along the transverse direction show oscillatory behavior. The power spectra and predominant frequencies of these distributions suggest the periodicity of the oscillations relates to multiples of local material length scales such as grain size. This demonstrates that the grain size in the samples, about 120 µm, is too large compared to the sample size to try to capture spatial variability due to applied loads and the microstructure, since the microstructure itself produces variability on the order of a few grain sizes. These results may play a role for the design of experiments to collect real-world 3D damage data for validating and enhancing the accuracy and definition of simulation models for damage characterization by providing frameworks for microstructural strain variability when modeling spall behavior under dynamic damage.
ContributorsNimbkar, Sharmila (Author) / Peralta, Pedro (Thesis director) / Oswald, Jay (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2023-12
Description
Recent satellite and remote sensing innovations have led to an eruption in the amount and variety of geospatial ice data available to the public, permitting in-depth study of high-definition ice imagery and digital elevation models (DEMs) for the goal of safe maritime navigation and climate monitoring. Few researchers have investigated texture in optical imagery as a predictive measure of Arctic sea ice thickness due to its cloud pollution, uniformity, and lack of distinct features that make it incompatible with standard feature descriptors. Thus, this paper implements three suitable ice texture metrics on 1640 Arctic sea ice image patches, namely (1) variance pooling, (2) gray-level co-occurrence matrices (GLCMs), and (3) textons, to assess the feasibly of a texture-based ice thickness regression model. Results indicate that of all texture metrics studied, only one GLCM statistic, namely homogeneity, bore any correlation (0.15) to ice freeboard.
ContributorsWarner, Hailey (Author) / Cochran, Douglas (Thesis director) / Jayasuria, Suren (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Electrical Engineering Program (Contributor)
Created2024-05
Description
Among classes in the Computer Science curriculum at Arizona State University, Automata Theory is widely considered to be one of the most difficult. Many Computer Science concepts have strong visual components that make them easier to understand. Binary trees, Dijkstra's algorithm, pointers, and even more basic concepts such as arrays all have very strong visual components. Not only that, but resources for them are abundantly available online. Automata Theory, on the other hand, is the first Computer Science course students encounter that has a significant focus on deep theory. Many
of the concepts can be difficult to visualize, or at least take a lot of effort to do so. Furthermore, visualizers for finite state machines are hard to come by. Because I thoroughly enjoyed learning about Automata Theory and parsers, I wanted to create a program that involved the two. Additionally, I thought creating a program for visualizing automata would help students who struggle with Automata Theory develop a stronger understanding of it.
ContributorsSmith, Andrew (Author) / Burger, Kevin (Thesis director) / Meuth, Ryan (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2021-12
Description
This thesis explores how large scale cyber exercises work in the 21st century, going in-depth on Exercise Cyber Shield, the Department of Defense’s largest unclassified cyber defense exercise run by the Army National Guard. It highlights why these cyber exercises are so relevant, going over several large scale cyber attacks that have occurred in the past year and the impact they caused. This research aims to illuminate the intricacies around cyber exercise assessment involving manual vs automated scoring systems; this is brought back to work on creating an automated scoring engine for Exercise Cyber Shield. This thesis provides an inside look behind the scenes of the operations of the largest unclassified cyber defense exercise in the United States, including conversations with the Exercise Officer-In-Charge of Cyber Shield as well as a cyber exercise expert working on assessment of Exercise Cyber Shield, and the research also includes information from past final reports for Cyber Shield. Issues that these large scale cyber exercises have faced over the years are brought to light, and attempts at solutions are discussed.
ContributorsZhao, Henry (Author) / Chavez Echeagaray, Maria Elena (Thesis director) / Rhodes, Brad (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2021-12
ContributorsPishko, Claire (Author) / Harelson, Haley (Co-author) / Doebbeling, Bradley (Thesis director) / Meja, Mauricio (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2021-12